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A code-generation path drafts edits and a stronger model checks them. Decide which spans are accepted or rolled back, keep the result correct, and show whether the second pass actually saves time. This independent, original scenario is designed for ML / Inference Systems, Research Engineer preparation. Work through the stages in order, explain each decision, and use the review dimensions to test whether the result remains defensible when constraints change.
State what must hold for an accepted span to be indistinguishable from the output of the slower path, and give the checks that would catch a wrong acceptance that still compiles.
Do you define correctness before measuring speed?A verifier rejects a span after dependent edits were already produced. Explain the rollback and what happens to the work that depended on it.
Can you restore state without corrupting the file?Design the experiment that compares this path against a non-speculative baseline: the workload, the metric, and the conditions under which the extra pass loses. State what result would make you drop the idea.
Is the speedup measured on a baseline a reviewer would accept?Use these checks after a first draft. They are review prompts, not six more questions to answer.
Clarifies goals, constraints, and ambiguous requirements.
Protects invariants and handles boundary conditions.
Identifies realistic failures and designs recovery paths.
Makes ownership and transitions explicit.
Provides enough visibility to diagnose and run the system.
Explains what is gained, lost, and intentionally deferred.
Use a different system, project, or situation and check whether the structure still produces a specific, defensible answer.